Harbor district communication optical cable intelligent operation and maintenance method and system based on AI technology

Abstract

The invention discloses a harbor area communication optical cable intelligent operation and maintenance method and system based on the AI technology. The method comprises the steps that optical cable sound wave vibration intensity parameters, environment parameters and positioning signal parameters are obtained in real time; performing quantitative preprocessing on each parameter and generating a vibration energy entropy coefficient, an environment stress coefficient and a positioning confidence coefficient; calculating a health index according to the optical cable health assessment model fusing the coefficients; triggering corresponding response measures according to the health indexes; generating a maintenance priority list, and performing path planning of a plurality of operation and maintenance nodes based on a Dijkstra algorithm; and updating the health assessment model. The system comprises a data acquisition module, a database storage module, an intelligent operation and maintenance center and an operation and maintenance module. According to the method, multi-dimensional sensing data is collected and quantified into corresponding coefficients, the optical cable concealment damage and potential risk sensing capacity is improved, the evaluation model is established to predict and accurately judge the operation state of the optical cable, active preventive and repairable maintenance is guided to be implemented, and the comprehensive operation and maintenance capacity of the system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of optical cable operation and maintenance algorithms, and specifically to an AI-based intelligent operation and maintenance method and system for port communication optical cables. Background Art

[0002] As global ports accelerate their intelligent and automated progress, the operational stability of port communication optical cables, as core data transmission infrastructure, directly determines the effectiveness of port logistics scheduling, equipment coordination, and security monitoring. However, traditional operations and maintenance models rely on manual inspections and passive troubleshooting, making it difficult to detect hidden damage and environmental stress on optical cables in real time. This leads to delayed fault response and high maintenance costs, severely hindering the efficient operation of ports.

[0003] Current optical cable operation and maintenance technology lacks comprehensiveness, and its predictive and disaster prevention capabilities are insufficient, making it impossible to make accurate and efficient maintenance decisions. Specifically, the following are reflected in the following aspects: First, the ability to integrate multi-source heterogeneous data is insufficient. For example, the integration and collaborative analysis capabilities of DAS acoustic temperature and humidity monitoring are weak, and the coupled effects of environmental stress and vibration intensity are not fully quantified, making it difficult to build a comprehensive optical cable status assessment model; second, the SDH alarm information fault analysis capability is insufficient. Through SDH alarm information, only a vague judgment of optical fiber faults can be made, and it is impossible to integrate multi-dimensional data and historical operation and maintenance data to improve the accuracy of fault judgment; third, there is a lack of dynamic self-optimization mechanism, and it is impossible to dynamically adjust the assessment model based on historical operation and maintenance data to improve operation and maintenance efficiency; fourth, maintenance strategies rely on manual experience and lack intelligent priority decisions based on multi-dimensional factors such as fault impact range, service interruption level, environmental risk, maintenance cost, etc., resulting in inefficient resource scheduling and difficulty in meeting the port's high real-time and high reliability operation and maintenance requirements.

[0004] Therefore, there is an urgent need for an intelligent operation and maintenance method and system for port communication optical cables based on AI technology to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to overcome the defects of the prior art and provide an intelligent operation and maintenance method and system for port communication optical cables based on AI technology to solve the problems raised in the above background technology.

[0006] In order to achieve the above object, the technical solution of the present invention is as follows:

[0007] An AI-based intelligent operation and maintenance method for port area communication optical cables includes the following contents and steps:

[0008] S1. Real-time acquisition of the acoustic vibration intensity parameters, environmental parameters, and positioning signal parameters of the optical cable;

[0009] S2. Quantify and pre-process the acoustic wave vibration intensity parameters, environmental parameters, and positioning signal parameters to generate a vibration energy entropy coefficient, an environmental stress coefficient, and a positioning signal coefficient, respectively;

[0010] S3. Establish an optical cable health assessment model that integrates the vibration energy entropy coefficient, environmental stress coefficient, and fixed position signal coefficient to generate a health index;

[0011] S4. Preset health index thresholds and response measures, determine the operation and maintenance node based on the health index value, and trigger the response measures;

[0012] S5. If there are multiple operation and maintenance nodes, a maintenance priority list is generated based on the fault impact range, service interruption level, environmental risk level, and equipment availability. Operation and maintenance paths are planned for the operation and maintenance nodes. Node edge weights are generated based on the maintenance priority, distance cost, time cost, and risk cost of the access node. Operation and maintenance path planning is performed based on the Dijkstra algorithm.

[0013] S6. Repair each of the operation and maintenance nodes according to the operation and maintenance path, and update the optical cable health assessment model after the processing is completed.

[0014] Furthermore, the acoustic vibration intensity parameter includes the signal amplitude, and the M-frame vibration signal of each set of vibration data is statistically analyzed and the vibration energy entropy coefficient is used. Characterize the energy distribution of vibration signals:

[0015] ;

[0016]

[0017]

[0018] in, is the energy probability of the mth frame, k is the energy sensitivity factor, is the energy of the mth frame, L is the number of signal sampling points in each frame, and s(n) is the signal amplitude of the nth sampling point.

[0019] Furthermore, environmental parameters include temperature and humidity. The impact of environmental parameters on optical cables is manifested as the cumulative stress caused by changes in temperature and humidity, expressed as the environmental stress coefficient. Perform comprehensive characterization:

[0020] ;

[0021] ;

[0022] ;

[0023] in, and are the weight coefficients of temperature and humidity respectively, and + =1; is the temperature accumulated stress, is the temperature influence coefficient, for The instantaneous temperature value at the time point, is the standard operating temperature of the optical cable; is the current time, is the initial time of statistics; is the humidity accumulated stress, is the humidity influence coefficient, for The instantaneous humidity value at a certain point in time.

[0024] Furthermore, the inherent error of the positioning terminal device is taken into account, and the positioning confidence coefficient C is calculated based on the signal strength and signal fluctuation:

[0025] ;

[0026] Signal strength influencing factors ;

[0027] Signal fluctuation impact factor ;

[0028] Equipment error influencing factors ;

[0029] in, 、 and are the weight coefficients of each influencing factor, and + + =1; is the signal strength of the current acquisition frame, is the maximum value of signal strength; is the signal adjustment coefficient, is the standard deviation of the signal fluctuation; To locate the terminal equipment adjustment coefficient, is the inherent error of the positioning terminal equipment.

[0030] Furthermore, the optical cable health assessment model is:

[0031] ;

[0032] in, is the health index, is the nonlinear adjustment factor, is the health threshold, 、 and They are the dynamic weight coefficients of vibration energy entropy coefficient, environmental stress coefficient and fixed position confidence coefficient respectively.

[0033] Furthermore, when the health index ≤ the health index threshold, triggering three-level compensation measures, specifically:

[0034] The first level is: start the backup optical fiber link to switch the data route;

[0035] The second level is to use thermal imaging technology to inspect along the line and search for optical cable fault points;

[0036] The third level is to perform local fiber optic splicing to repair the optical cable fault point.

[0037] Furthermore, the method further includes fault analysis, collecting SDH alarm information, system error information, and optical power information data, and performing fault analysis based on the vibration energy entropy coefficient, the environmental stress coefficient, and the fixed position signal coefficient. The analysis method includes:

[0038] 1) If an R-Los alarm occurs, the vibration energy entropy coefficient changes suddenly, and the fixed position confidence coefficient is abnormally high, combined with the presence of a break in the historical fault data of the fault reporting point, the fault at the fault reporting point is determined to be a cable break;

[0039] 2) For system error alarms, calculate the bit error rate. If the bit error rate fluctuates periodically and is related to the change cycle of the environmental parameters, combine the environmental stress coefficient within the time period. When the environmental stress coefficient exceeds the environmental stress threshold, it is determined that the bit error fault is a fault caused by environmental influence;

[0040] 3) For the system bit error alarm, if the bit error rate suddenly increases without obvious periodicity, and the R-Los alarm does not appear, but there is abnormal fluctuation in optical power, it is determined that the fault point is optical cable aging or optical cable failure.

[0041] Furthermore, the maintenance priority list is created based on the Mamdani fuzzy reasoning method. Fuzzy sets are assigned to the fault impact range, service interruption level, environmental risk level, and equipment availability status. These fuzzy sets are combined to establish a maintenance priority rule base, matching maintenance priorities for each operation and maintenance node.

[0042] Different maintenance priorities match different cost impact factors A multi-objective cost function is established, and the cost value is used as the node edge weight from the current position to the access node, and the operation and maintenance path planning is performed based on the Dijkstra algorithm;

[0043] ;

[0044] Wherein, a, b and c are weight coefficients of distance cost, time cost and risk cost respectively. is the physical distance from the current location to the access node, is the time taken to reach the access node from the current location, is a comprehensive risk assessment value in the process of reaching the access node from the current location.

[0045] Furthermore, after the repair of the operation and maintenance node is completed, the real health index of the optical cable is re-evaluated, and a loss function is established. The nonlinear adjustment factor, health threshold and dynamic weight coefficient are gradually adjusted according to the gradient direction of the error, and the prediction error is minimized according to the gradient descent algorithm. , optimize and update the optical cable health assessment model; among them,

[0046] Loss function: ;

[0047] Among them, n is the training sample, which comes from the repaired node data and historical node data; is the predicted value of the health index based on the i-th sample data, is the true value of the health index based on the i-th sample data.

[0048] An intelligent operation and maintenance system for port area communication optical cables, including a data acquisition module, an AI analysis terminal, a database storage module, an intelligent operation and maintenance hub, and an operation and maintenance module; wherein,

[0049] The data acquisition module includes: acoustic wave sensor DNS equipment, SDH transmission equipment, environmental monitoring instruments, and positioning terminals;

[0050] AI analysis terminal, used to calculate the vibration energy entropy coefficient, environmental stress coefficient, and positioning signal coefficient based on real-time monitoring data, and send it to the intelligent operation and maintenance center;

[0051] The database storage module includes: a time series database for receiving and storing real-time monitoring data uploaded by the data acquisition module; a relational database for storing operation and maintenance data uploaded by the operation and maintenance module and the parameters and definitions of each algorithm model; an optical cable status feature database containing optical cable abnormal event types, fault location mapping relationships and maintenance strategy knowledge base;

[0052] An intelligent operation and maintenance center is configured to receive and process the real-time monitoring data from the data acquisition module, calculate a health index based on the vibration energy entropy coefficient, the environmental stress coefficient, and the fixed position signal coefficient, and generate a maintenance priority list; the intelligent operation and maintenance center analyzes the cause of the fault based on the coefficients and the information data from the acoustic wave sensing DNS device;

[0053] The operation and maintenance module includes: a scheduling terminal for receiving the maintenance priority list and the positioning information of the positioning terminal, and generating scheduling instructions based on the built-in cost analysis algorithm and operation and maintenance strategy; a drone swarm for conducting thermal imaging inspections of optical cables according to the scheduling instructions; an intelligent robot for performing optical fiber fusion repair work according to the scheduling instructions; and maintenance personnel for performing deep repair work according to the scheduling instructions.

[0054] Compared with the existing technology, the AI-based intelligent operation and maintenance method and system for port area communication optical cables of the present invention have the following beneficial effects:

[0055] 1. This operation and maintenance method improves the ability to perceive potential risks caused by hidden damage and environmental stress of optical cables by introducing the vibration energy entropy coefficient, environmental stress coefficient and fixed position signal coefficient, and combines the three coefficients to establish an evaluation model to calculate the health index. It can predict and accurately judge the operating status of optical cables, implement different operation and maintenance strategies based on the health index, and guide the appropriate implementation of proactive preventive maintenance and corrective maintenance. Fault response is fast and handled appropriately, reducing potential fault risks, reducing operation and maintenance costs, improving the comprehensive operation and maintenance capabilities of optical cables, and enhancing the overall resilience of the port optical cable network strategy; through path optimization, maintenance resources are reasonably scheduled, and the evaluation model is automatically updated and optimized after maintenance to improve the adaptability of the evaluation model.

[0056] 2. The system collects and integrates multi-source heterogeneous data, calls different operation and maintenance strategies according to the health of the optical cable, comprehensively analyzes the cause of the fault based on historical data and collected data, and the dispatch terminal generates instructions for comprehensive operation and maintenance and dispatches maintenance equipment and personnel. It also optimizes paths for multiple fault points, reasonably allocates maintenance resources, and reduces the impact of faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a flow chart of the intelligent operation and maintenance method for port area communication optical cables disclosed in the present invention;

[0058] Figure 2 This is a schematic diagram of the composition of the intelligent operation and maintenance system for port area communication optical cables disclosed in the present invention. DETAILED DESCRIPTION

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only the best embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0060] References to "embodiments" herein mean that the specific methods, steps, or content described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of such phrases in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive with other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0061] This embodiment provides an AI-based intelligent operation and maintenance method for port area communication optical cables. Figure 1 As shown in the figure, based on the intelligent operation and maintenance system of the port area communication optical cable, Figure 2 As shown, the operation and maintenance system includes a data acquisition module, a database storage module, an AI analysis terminal, an intelligent operation and maintenance hub, and an operation and maintenance module. The following describes the implementation of the operation and maintenance method in conjunction with the operation and maintenance system. Specifically, it includes the following contents and steps:

[0062] S1. First, build a database storage module. The database storage module uses a cloud database, which includes a time series database, a relational database, and an optical cable status feature database. The time series database InfluxDB receives and stores high-frequency real-time monitoring data uploaded by the data acquisition module. The relational database MySQL records and stores the operation and maintenance data uploaded by the operation and maintenance module, the coefficient calculation formulas and health index model parameters, fuzzy set definitions, etc., to facilitate the intelligent operation and maintenance center to call. The optical cable status feature database records the types of historical abnormal events of optical cables, fault location mapping relationships, and maintenance strategy knowledge base.

[0063] Secondly, the data acquisition module collects the acoustic vibration intensity parameters, environmental parameters, and positioning signal parameters of the optical cable in real time and uploads them to the time series database InfluxDB. In this embodiment, the data acquisition module specifically includes distributed optical fiber acoustic wave sensor DNS equipment, SDH transmission equipment, environmental monitoring instruments, and positioning terminals.

[0064] Distributed fiber optic acoustic wave sensing (DNS) equipment is based on the scattering principle. When an external sound wave or vibration acts on the optical cable, it will cause local vibration of the optical fiber, thereby changing the phase of the Rayleigh scattered light. The signal processing device demodulates and extracts the phase change of the vibration signal to obtain the acoustic wave vibration intensity parameter, that is, the signal amplitude.

[0065] SDH transmission equipment receives optical signals from optical fibers. It has a built-in optical power monitoring function that can monitor the optical signal strength in real time. When the received optical signal strength is lower than the preset minimum threshold of received optical power, the optical board R-LOS alarm is triggered. At the same time, the SDH transmission equipment uses an error detection algorithm (such as BIP-8) to detect bit errors in the transmission signal in real time and calculate the bit error rate (BER). , is the number of error symbols, The total number of transmitted symbols. When the bit error rate exceeds the preset threshold, the system error alarm is triggered.

[0066] Environmental monitoring instruments are temperature sensors and humidity sensors, which are installed at key nodes along the optical cable line to collect temperature and humidity data of each node in real time;

[0067] The positioning terminal, consisting of a satellite positioning system and a signal strength sensor, is used to obtain the location coordinate data of key nodes or fault points, monitor the signal strength in real time, and calculate the positioning confidence coefficient of the coordinate point based on the signal strength;

[0068] It should be noted that distributed fiber optic acoustic sensor DNS equipment, environmental monitoring instruments, positioning terminals and AI analysis terminals are arranged along the port area's communication optical cables to collect real-time optical cable status signal data at each key location.

[0069] S2, the AI ​​analysis terminal quantifies and pre-processes the acoustic vibration intensity parameters, environmental parameters, and positioning signal parameters of its focus area, generating vibration energy entropy coefficients, environmental stress coefficients, and positioning signal coefficients, respectively. It also analyzes the causes of SDH alarm information and system error information.

[0070] Quantitative preprocessing of acoustic vibration intensity parameters: statistics are performed on the M-frame signals of each set of vibration data, and the vibration energy entropy coefficient is used Characterize the energy distribution of vibration signals:

[0071] ;

[0072]

[0073]

[0074] in, is the energy probability of the mth frame, k is the energy sensitivity factor, and its value range is , the default value is 1.5, is the energy of the mth frame, L is the number of sampling points of each frame signal, and s(n) is the signal amplitude of the nth sampling point;

[0075] Vibrational energy entropy coefficient The distribution law of the vibration signal is reflected. The larger the value, the more dispersed the energy distribution of the optical cable vibration signal is, indicating that the optical cable has multi-source interference or is subjected to abnormal impact, and the risk of optical cable breakage is greater; vibration energy entropy coefficient The smaller the value, the more balanced the energy distribution of the optical cable vibration signal, indicating that the optical cable is in good working condition.

[0076] Quantitative preprocessing of environmental parameters: The impact of environmental parameters on optical cables is manifested as cumulative stress caused by changes in temperature and humidity, expressed as the environmental stress coefficient. Perform comprehensive characterization:

[0077] ;

[0078] ;

[0079] ;

[0080] in, and are the weight coefficients of temperature and humidity respectively, and + =1; is the temperature accumulated stress, is the temperature influence coefficient, for The instantaneous temperature value at the time point, , is the standard operating temperature of the optical cable, and A is the amplitude of temperature fluctuation; is the current time, is the initial time of statistics; is the humidity accumulated stress, is the humidity influence coefficient, for The instantaneous humidity value at the time point, , is the standard operating humidity of the optical cable, and B is the amplitude of humidity fluctuation;

[0081] Environmental stress coefficient The larger the value, the greater the cumulative stress of environmental changes, indicating that the impact of the environment on the optical cable is more serious and the risk of optical cable failure or breakage is greater;

[0082] Quantitative preprocessing of positioning signal parameters: The accuracy of the positioning signal is characterized by the positioning confidence coefficient C, which comprehensively considers the impact of the inherent error of the positioning terminal device, signal strength, and signal fluctuation on positioning:

[0083] ;

[0084] Signal strength influencing factors ;

[0085] Signal fluctuation impact factor ;

[0086] Equipment error influencing factors ;

[0087] in, 、 and are the weight coefficients of each influencing factor, and + + =1; is the signal strength of the current acquisition frame, is the maximum value of signal strength; is the signal adjustment coefficient, is the standard deviation of signal fluctuation; Adjustment coefficient for positioning terminal equipment;

[0088] is the inherent error of the positioning terminal equipment, , is the mean error, for a known position ( ) to perform n measurements and calculate the coordinate value of each measurement ( ) , ,but , is the standard deviation of the error, that is ;

[0089] Signal strength influencing factors The higher the value, the closer the current signal strength is to the maximum value, indicating that the signal transmission is more stable and the positioning accuracy is higher; the signal fluctuation influence factor Indicates the signal fluctuation over a period of time. The higher the value, the more stable the signal and the higher the positioning reliability. The inherent error of the positioning terminal device The larger the value, the smaller the device error. The positioning confidence coefficient C quantifies the influence of signal strength, signal fluctuation and device error. The larger the value, the higher the positioning reliability, the more accurate and stable the positioning result, and the higher the credibility of the positioning data.

[0090] The intelligent operation and maintenance center analyzes the cause of the fault based on the SDH alarm information and system error information of the SDH transmission equipment by integrating the vibration energy entropy coefficient, environmental stress coefficient, and fixed position signal coefficient. The analysis method is as follows:

[0091] 1) If an R-Los alarm occurs and the vibration energy entropy coefficient changes suddenly and the location confidence coefficient is abnormally high, combined with the presence of a break in the historical fault data at the fault point, the fault at the node is determined to be a fiber optic cable break. The location coordinates of the fault point are sent to the operation and maintenance module, and the operation and maintenance node path planning is carried out based on the operation and maintenance priority list. A sudden change refers to a significant abnormal change in the vibration energy entropy coefficient in a very short period of time.

[0092] 2) For system error alarms, calculate the bit error rate (BER). If the bit error rate fluctuates periodically and is related to the temperature or humidity change cycle of the environmental parameters, combine it with the environmental stress coefficient during the time period. When the environmental stress coefficient When the environmental stress threshold is exceeded, the bit error fault is determined to be a fault caused by environmental influence;

[0093] 3) For system bit error alarms, if the bit error rate suddenly increases without obvious periodicity and no R-Los alarm occurs, but the optical power monitoring of the SDH transmission equipment shows abnormal fluctuations in optical power, the fault is judged to be optical cable aging or optical cable failure.

[0094] S3. Integrate the vibration energy entropy coefficient, environmental stress coefficient and fixed position signal coefficient to establish a cable health assessment model and calculate the health index of the cable. ;

[0095] ;

[0096] in, is the nonlinear adjustment factor, , the default value is 0.5; is the health threshold, , The default value is 0.5 according to the importance of the port area, and the core area should be appropriately increased. value; 、 and are the dynamic weight coefficients of vibration energy entropy coefficient, environmental stress coefficient and fixed position confidence coefficient respectively, and their initial default values ​​are =0.65, =0.25, =0.1;

[0097] Health Index The smaller the value, the worse the health of the optical cable, and vice versa.

[0098] S4. The maintenance strategy knowledge base records operation and maintenance strategies that match different optical cable health states, sets health index thresholds and optical cable operation and maintenance thresholds at all levels, and different health indexes trigger different response measures, as shown in the example in Table 1.

[0099] Table 1 Response measures for fiber optic cable health status classification

[0100]

[0101] Among them, for the operation and maintenance strategy with the three-level compensation measures as the response measure, the intelligent operation and maintenance center generates a maintenance priority list and sends it to the operation and maintenance modules in the corresponding areas. There are multiple operation and maintenance modules, which are set up at key nodes along the port area communication optical cables. They include dispatching terminals, maintenance equipment and maintenance personnel. The maintenance equipment includes drone swarms and intelligent robots. According to the maintenance instructions, the dispatching terminal initiates the three-level compensation measures, which specifically include the following:

[0102] The first level is: the dispatch terminal starts the backup optical fiber link to switch the data route. The backup optical fiber link adopts dynamic load balancing technology, with a switching time of ≤50ms. It can support multi-link aggregation and has an automatic recovery mechanism. The fault recovery time is ≤2 minutes and the total bandwidth is ≥10Gbps.

[0103] The second level is: the dispatch terminal dispatches a group of drones to conduct thermal imaging inspections along the fault area to search for and determine the fault point;

[0104] The third level is: the dispatch terminal deploys an intelligent robot to perform local fiber fusion splicing at the fault point to repair the fault; record the repair data and update the cloud database;

[0105] It is important to know that if the compensation measures do not meet expectations, the dispatch terminal will automatically dispatch maintenance personnel to perform in-depth repairs. After the repairs are completed, the maintenance personnel will update the cloud database.

[0106] S5. If there are multiple operation and maintenance nodes that require compensation measures, the intelligent operation and maintenance hub generates a maintenance priority list and assigns corresponding priority cost impact values. The dispatching terminal calculates the node edge weight based on the priority cost impact value, distance cost, time cost, and risk cost of the access node, and performs operation and maintenance path planning based on the Dijkstra algorithm.

[0107] The maintenance priority list is determined based on the Mamdani reasoning method, and its influencing factors include the fault impact range, service interruption level, environmental risk level, and equipment availability. In a specific embodiment of this example, as shown in Tables 2 to 5, the membership function formula of each influencing factor is established to determine its fuzzy set;

[0108] Table 2 Fuzzy set table of fault impact range

[0109]

[0110] Table 3 Fuzzy set table of service interruption level

[0111]

[0112] Table 4 Fuzzy set table of environmental risk levels

[0113]

[0114] Table 5 Fuzzy set table of available equipment status

[0115]

[0116] Tables 2-5 not only provide definitions of the influencing item fuzzy sets, but also provide membership function formulas. By calculating the membership of the influencing items, multiple operation and maintenance nodes within the same trigger rule can be arranged in priority queues based on the membership. A preset rule base is used for reasoning and outputs maintenance priorities. In a specific embodiment of this example, the preset rule base is shown in Table 6.

[0117] Table 6 Rule base table

[0118]

[0119] Based on the maintenance priority list output by the intelligent operation and maintenance center, the dispatch terminal of the operation and maintenance module retrieves the location information of the optical cable fault point, including the location information of the fault point determined by the fault analysis to be a break, determines the target locations of the maintenance task, and plans the optimal path. At the same time, through communication with the satellite positioning system, it obtains the location information of the equipment or personnel performing the maintenance task (such as intelligent robots, drone swarms, and maintenance personnel), which is used as the starting point for path planning.

[0120] Quantify the maintenance priority of operation and maintenance nodes as cost impact factors , where maintenance priority is the cost impact factor of urgency =0.3, cost impact factor for high maintenance priority =0.6, cost impact factor for low maintenance priority = 0.9, establish the multi-objective cost function for path planning:

[0121]

[0122] Among them, a, b and c are the weight coefficients of each cost influencing item, a+b+c=1, the default values ​​are a=0.7, b=0.2, c=0.1, is the physical distance from the current location to the access node, is the time taken to reach the access node from the current location, To evaluate the overall risk during the execution of this path, , the default value is 0.5;

[0123] The dispatching terminal uses the cost value as the edge weight from the current location to the access node, integrates the port area's geographic information, road distribution conditions, optical cable laying routes and traffic rules of each area, and constructs a feasible path network diagram containing all operation and maintenance nodes. It uses the Dijkstra algorithm to calculate the minimum cost path from the starting point to each operation and maintenance node, generates a detailed path planning plan, and dispatches drone swarms to perform thermal imaging inspections in real time and dispatches intelligent robots to perform fiber optic fusion operations. If the compensation measures do not meet expectations, maintenance personnel will be dispatched for in-depth maintenance. After the task is completed, the maintenance path, time, fault handling details, and equipment status analysis results will be uploaded to the database storage module.

[0124] S6. After completing the repair process of the operation and maintenance node, based on the repair result data and historical data, update and optimize the health assessment model of the intelligent operation and maintenance center;

[0125] Specifically, a loss function is established, and the real health index is re-evaluated after the operation and maintenance node is repaired. The collected data after repair is used as part of the sample, combined with the historical sample data, and the nonlinear adjustment factor, health threshold and various dynamic weight coefficients are gradually adjusted according to the gradient direction of the error. The prediction error is minimized according to the gradient descent algorithm, and the health assessment model is optimized and updated;

[0126] Loss function: ;

[0127] Among them, n is the training sample, which comes from the repaired node data and historical node data; is the predicted value of the health index based on the i-th sample data, is the true value of the health index based on the i-th sample data.

[0128] Through the description of the above embodiments, those skilled in the art can clearly understand that the various embodiments of the present application can be implemented by means of software or software combined with a necessary general hardware platform, and of course can also be implemented by hardware functions. Based on such understanding, the technical solution of the present application can essentially be embodied in the form of a software product or the part that contributes to the prior art. The software product is stored in a storage medium and includes a number of instructions for enabling a computer device, such as but not limited to a personal computer, a server, or a network device, to execute all or part of the steps of the method described in any embodiment of the present application.

[0129] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An AI-based intelligent operation and maintenance method for port area communication optical cables, characterized in that: Includes the following: S1. Real-time acquisition of the acoustic vibration intensity parameters, environmental parameters, and positioning signal parameters of the optical cable; S2. quantify and preprocess the acoustic wave vibration intensity parameter, the environmental parameter, and the positioning signal parameter to generate a vibration energy entropy coefficient, an environmental stress coefficient, and a positioning signal coefficient, respectively; S3. Establishing an optical cable health assessment model that integrates the vibration energy entropy coefficient, the environmental stress coefficient, and the fixed position confidence coefficient to generate a health index; S4. Preset health index thresholds and response measures, determine the operation and maintenance node based on the health index value, and trigger the response measures; S5. If there are multiple operation and maintenance nodes, a maintenance priority list is generated based on the fault impact range, service interruption level, environmental risk level, and equipment availability. Operation and maintenance paths are planned for the operation and maintenance nodes. Node edge weights are generated based on the maintenance priority, distance cost, time cost, and risk cost of the access node. Operation and maintenance path planning is performed based on the Dijkstra algorithm. S6. Repair each of the operation and maintenance nodes according to the operation and maintenance path, and update the optical cable health assessment model after the processing is completed.

2. The AI-based intelligent operation and maintenance method for port area communication optical cables according to claim 1 is characterized by: The acoustic vibration intensity parameter includes signal amplitude, and statistics are performed on the M frames of vibration signals of each set of vibration data, and the vibration energy entropy coefficient is used as the Characterize the energy distribution of the vibration signal: ； ； ； in, is the energy probability of the mth frame, k is the energy sensitivity factor, is the energy of the mth frame, L is the number of signal sampling points in each frame, and s(n) is the signal amplitude of the nth sampling point.

3. The AI-based intelligent operation and maintenance method for port area communication optical cables according to claim 2 is characterized by: The environmental parameters include temperature and humidity. The influence of the environmental parameters on the optical cable is manifested as the cumulative stress caused by the change of temperature and humidity. Perform comprehensive characterization: ； ； ； in, and are the weight coefficients of temperature and humidity respectively, and + =1; is the temperature accumulated stress, is the temperature influence coefficient, for The instantaneous temperature value at the time point, is the standard operating temperature of the optical cable; is the current time, is the initial time of statistics; is the humidity accumulated stress, is the humidity influence coefficient, for The instantaneous humidity value at a certain point in time.

4. The AI-based intelligent operation and maintenance method for port area communication optical cables according to claim 3 is characterized by: Taking into account the inherent error of the positioning terminal device, the positioning confidence coefficient C is calculated based on the signal strength and signal fluctuation: ； Signal strength influencing factors ; Signal fluctuation impact factor ; Equipment error influencing factors ; in, 、 and are the weight coefficients of each influencing factor, and + + =1; is the signal strength of the current acquisition frame, is the maximum value of signal strength; is the signal adjustment coefficient, is the standard deviation of the signal fluctuation; is the adjustment coefficient of the positioning terminal device, is the inherent error of the positioning terminal device.

5. The AI-based intelligent operation and maintenance method for port area communication optical cables according to claim 4 is characterized by: The optical cable health assessment model is: ； in, is the health index, is the nonlinear adjustment factor, is the health threshold, 、 and are the dynamic weight coefficients of the vibration energy entropy coefficient, the environmental stress coefficient and the fixed position confidence coefficient respectively.

6. The AI-based intelligent operation and maintenance method for port area communication optical cables according to claim 5 is characterized by: When the health index ≤ the health index threshold, triggering three-level compensation measures, specifically: The first level is: start the backup optical fiber link to switch the data route; The second level is to use thermal imaging technology to inspect along the line and search for optical cable fault points; The third level is: performing local fiber fusion splicing to repair the optical cable fault point.

7. The AI-based intelligent operation and maintenance method for port area communication optical cables according to claim 5 is characterized by: The method also includes fault analysis, collecting SDH alarm information, system error information and optical power information data, and performing fault analysis by integrating the vibration energy entropy coefficient, the environmental stress coefficient and the fixed position signal coefficient. The analysis method includes: 1) If an R-Los alarm occurs, the vibration energy entropy coefficient changes suddenly, and the fixed position confidence coefficient is abnormally high, combined with the presence of a break in the historical fault data of the fault reporting point, the fault at the fault reporting point is determined to be a cable break; 2) For system bit error alarms, calculate the bit error rate. If the bit error rate fluctuates periodically and is related to the change period of the environmental parameter, combined with the change of the environmental stress coefficient within the time period, when the environmental stress coefficient exceeds the environmental stress threshold, determine that the bit error fault is caused by environmental influences; 3) For the system bit error alarm, if the bit error rate suddenly increases without obvious periodicity, and the R-Los alarm does not appear, but there is abnormal fluctuation in optical power, it is determined that the fault point is optical cable aging or optical cable failure.

8. The AI-based intelligent operation and maintenance method for port area communication optical cables according to claim 6 is characterized by: The maintenance priority list is created based on the Mamdani fuzzy reasoning method. Fuzzy sets are assigned to the fault impact range, the service interruption level, the environmental risk level, and the equipment availability status. The fuzzy sets are combined to establish a rule base for the maintenance priority, and the maintenance priority is matched to each operation and maintenance node. Different maintenance priorities match different cost impact factors value, establish a multi-objective cost function, use the cost value as the node edge weight from the current position to the access node, and perform the operation and maintenance path planning based on the Dijkstra algorithm; ； Wherein, a, b and c are weight coefficients of the distance cost, the time cost and the risk cost respectively. is the physical distance from the current location to the access node, is the time taken to reach the access node from the current location, is a comprehensive risk assessment value in the process of reaching the access node from the current location.

9. The AI-based intelligent operation and maintenance method for port area communication optical cables according to claim 5 is characterized by: After the repair of the operation and maintenance node is completed, the real health index of the optical cable is re-evaluated, and a loss function is established. The nonlinear adjustment factor, the health threshold and the dynamic weight coefficient are gradually adjusted according to the gradient direction of the error, and the prediction error is minimized according to the gradient descent algorithm. , optimize and update the optical cable health assessment model; wherein, Loss function: ; Among them, n is the training sample, which comes from the repaired node data and historical node data; is the predicted value of the health index based on the i-th sample data, is the true value of the health index based on the i-th sample data.

10. An intelligent operation and maintenance system for port area communication optical cables, characterized by: It includes data acquisition module, AI analysis terminal, database storage module, intelligent operation and maintenance center and operation and maintenance module; among them, The data acquisition module includes: acoustic wave sensor DNS equipment, SDH transmission equipment, environmental monitoring equipment, and positioning terminal; The AI ​​analysis terminal is used to calculate the vibration energy entropy coefficient, environmental stress coefficient and fixed position signal coefficient based on real-time monitoring data, and send them to the intelligent operation and maintenance center; The database storage module includes: a time series database for receiving and storing the real-time monitoring data uploaded by the data acquisition module; a relational database for storing the operation and maintenance data uploaded by the operation and maintenance module and the parameters and definitions of each algorithm model; an optical cable status feature database, including optical cable abnormal event types, fault location mapping relationships, and a maintenance strategy knowledge base; The intelligent operation and maintenance center is used to receive and process the real-time monitoring data from the data acquisition module, calculate the health index based on the vibration energy entropy coefficient, the environmental stress coefficient and the fixed position signal coefficient, and generate a maintenance priority list; the intelligent operation and maintenance center analyzes the cause of the fault based on the coefficients and the information data of the acoustic wave sensing DNS device; The operation and maintenance module includes: a scheduling terminal, which is used to receive the maintenance priority list and the positioning information of the positioning terminal, and generate scheduling instructions based on the built-in cost analysis algorithm and operation and maintenance strategy; a drone swarm, which is used to perform thermal imaging inspections of optical cables according to the scheduling instructions; an intelligent robot, which is used to perform optical fiber fusion repair work according to the scheduling instructions; and maintenance personnel, which is used to perform deep repair work according to the scheduling instructions.

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